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您的检索式：作者名="Ziling Xing"
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| 1 | Research on the Best Shooting State Based on the “Three Forces” Model显示文摘The shooting state during shooting refers to the basketball’s shooting speed,shooting angle and the ball’s rotation speed.The basketball flight path is also related to these factors.In this paper,based on the three forces of Gravity,Air Resistance and Magnus Force,the“Three Forces”model is established,the Kinetic equations are derived,the basketball flight trajectory is solved by simulation,and the best shot state when shooting is obtained through the shooting percentage.Compared with the“Single Force”model that only considers Gravity,the shooting percentage of the“Three Forces”model is higher.The reason is that the Magnus Force generated by considering the basketball rotation speed is considered.Although in the“Three Forces”model,the shot speed is faster and the shot is harder,the backspin will reduce the angle of the shot and achieve the goal of saving effort.By calculating the best shot state and giving the athlete’s usual training state range,you can guide the training,thereby improving the athlete’s shooting percentage during the game. | Xuguang Liu Ruqing Zhao Qifei Chen Ming Shi Ziling Xing Yanan Zhang | 2020 | Journal on Big Data2020，2，2： | 0 |
| 2 | Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening显示文摘Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees. | Xing Wei Jinnuo Zhang Anna O.Conrad Charles E.Flower Cornelia C.Pinchot Nancy Hayes-Plazolles Ziling Chen Zhihang Song Songlin Fei Jian Jin | 2023 | Artificial Intelligence in Agriculture2023，，4： | 0 |
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